Mobile Sensing and Behavior Modeling.
Mobile sensing is one of the most widely available data sources for longitudinal human behavior modeling [1]}, [2]}, [3]}, [4]}, [5]}, [6]}.
Compared to traditional time-series data, mobile sensing data are much longer and uncontrolled (and thus have a high data missing rate [7]}). Moreover, the ground truth is usually much more sparse (e.g., self-report mental health measures administered weekly or less frequently  [8]}, [9]}).
Most existing human behavior modeling algorithms using mobile sensing data are not open-sourced and do not investigate cross-dataset generalization  [10]}, [11]}, [12]}, [13]}, [14]}, [15]}.

To date, there are only a few public longitudinal human behavior sensing datasets [16]}, [17]}, [18]}. Table REF  summarizes and compares them against our multi-year datasets. Existing passive mobile sensing datasets contain fewer than 50 participants and cannot support cross-dataset analysis.
They cannot serve as a golden benchmark for future proposed algorithms.
We are the first to release multi-year mobile sensing datasets to support the ML community in investigating cross-dataset generalizable behavior modeling algorithms.
